# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Model Checkpointing
===================
Automatically save model checkpoints during training.
"""
import logging
import os
import re
import shutil
import time
import warnings
from copy import deepcopy
from datetime import timedelta
from pathlib import Path
from typing import Any, Dict, Optional, Set
from weakref import proxy
import torch
import yaml
from torch import Tensor
import lightning.pytorch as pl
from lightning.fabric.utilities.cloud_io import _is_dir, get_filesystem
from lightning.fabric.utilities.types import _PATH
from lightning.pytorch.callbacks import Checkpoint
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_info, rank_zero_warn
from lightning.pytorch.utilities.types import STEP_OUTPUT
log = logging.getLogger(__name__)
warning_cache = WarningCache()
[docs]class ModelCheckpoint(Checkpoint):
r"""Save the model periodically by monitoring a quantity. Every metric logged with
:meth:`~lightning.pytorch.core.LightningModule.log` or :meth:`~lightning.pytorch.core.LightningModule.log_dict` is
a candidate for the monitor key. For more information, see :ref:`checkpointing`.
After training finishes, use :attr:`best_model_path` to retrieve the path to the
best checkpoint file and :attr:`best_model_score` to retrieve its score.
Args:
dirpath: directory to save the model file.
Example::
# custom path
# saves a file like: my/path/epoch=0-step=10.ckpt
>>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
By default, dirpath is ``None`` and will be set at runtime to the location
specified by :class:`~lightning.pytorch.trainer.trainer.Trainer`'s
:paramref:`~lightning.pytorch.trainer.trainer.Trainer.default_root_dir` argument,
and if the Trainer uses a logger, the path will also contain logger name and version.
filename: checkpoint filename. Can contain named formatting options to be auto-filled.
Example::
# save any arbitrary metrics like `val_loss`, etc. in name
# saves a file like: my/path/epoch=2-val_loss=0.02-other_metric=0.03.ckpt
>>> checkpoint_callback = ModelCheckpoint(
... dirpath='my/path',
... filename='{epoch}-{val_loss:.2f}-{other_metric:.2f}'
... )
By default, filename is ``None`` and will be set to ``'{epoch}-{step}'``, where "epoch" and "step" match
the number of finished epoch and optimizer steps respectively.
monitor: quantity to monitor. By default it is ``None`` which saves a checkpoint only for the last epoch.
verbose: verbosity mode. Default: ``False``.
save_last: When ``True``, saves a `last.ckpt` whenever a checkpoint file gets saved. On a local filesystem,
this will be a symbolic link, and otherwise a copy of the checkpoint file. This allows accessing the latest
checkpoint in a deterministic manner. Default: ``None``.
save_top_k: if ``save_top_k == k``,
the best k models according to the quantity monitored will be saved.
if ``save_top_k == 0``, no models are saved.
if ``save_top_k == -1``, all models are saved.
Please note that the monitors are checked every ``every_n_epochs`` epochs.
if ``save_top_k >= 2`` and the callback is called multiple
times inside an epoch, the name of the saved file will be
appended with a version count starting with ``v1``
unless ``enable_version_counter`` is set to False.
mode: one of {min, max}.
If ``save_top_k != 0``, the decision to overwrite the current save file is made
based on either the maximization or the minimization of the monitored quantity.
For ``'val_acc'``, this should be ``'max'``, for ``'val_loss'`` this should be ``'min'``, etc.
auto_insert_metric_name: When ``True``, the checkpoints filenames will contain the metric name.
For example, ``filename='checkpoint_{epoch:02d}-{acc:02.0f}`` with epoch ``1`` and acc ``1.12`` will resolve
to ``checkpoint_epoch=01-acc=01.ckpt``. Is useful to set it to ``False`` when metric names contain ``/``
as this will result in extra folders.
For example, ``filename='epoch={epoch}-step={step}-val_acc={val/acc:.2f}', auto_insert_metric_name=False``
save_weights_only: if ``True``, then only the model's weights will be
saved. Otherwise, the optimizer states, lr-scheduler states, etc are added in the checkpoint too.
every_n_train_steps: Number of training steps between checkpoints.
If ``every_n_train_steps == None or every_n_train_steps == 0``, we skip saving during training.
To disable, set ``every_n_train_steps = 0``. This value must be ``None`` or non-negative.
This must be mutually exclusive with ``train_time_interval`` and ``every_n_epochs``.
train_time_interval: Checkpoints are monitored at the specified time interval.
For all practical purposes, this cannot be smaller than the amount
of time it takes to process a single training batch. This is not
guaranteed to execute at the exact time specified, but should be close.
This must be mutually exclusive with ``every_n_train_steps`` and ``every_n_epochs``.
every_n_epochs: Number of epochs between checkpoints.
This value must be ``None`` or non-negative.
To disable saving top-k checkpoints, set ``every_n_epochs = 0``.
This argument does not impact the saving of ``save_last=True`` checkpoints.
If all of ``every_n_epochs``, ``every_n_train_steps`` and
``train_time_interval`` are ``None``, we save a checkpoint at the end of every epoch
(equivalent to ``every_n_epochs = 1``).
If ``every_n_epochs == None`` and either ``every_n_train_steps != None`` or ``train_time_interval != None``,
saving at the end of each epoch is disabled
(equivalent to ``every_n_epochs = 0``).
This must be mutually exclusive with ``every_n_train_steps`` and ``train_time_interval``.
Setting both ``ModelCheckpoint(..., every_n_epochs=V, save_on_train_epoch_end=False)`` and
``Trainer(max_epochs=N, check_val_every_n_epoch=M)``
will only save checkpoints at epochs 0 < E <= N
where both values for ``every_n_epochs`` and ``check_val_every_n_epoch`` evenly divide E.
save_on_train_epoch_end: Whether to run checkpointing at the end of the training epoch.
If this is ``False``, then the check runs at the end of the validation.
enable_version_counter: Whether to append a version to the existing file name.
If this is ``False``, then the checkpoint files will be overwritten.
Note:
For extra customization, ModelCheckpoint includes the following attributes:
- ``CHECKPOINT_JOIN_CHAR = "-"``
- ``CHECKPOINT_EQUALS_CHAR = "="``
- ``CHECKPOINT_NAME_LAST = "last"``
- ``FILE_EXTENSION = ".ckpt"``
- ``STARTING_VERSION = 1``
For example, you can change the default last checkpoint name by doing
``checkpoint_callback.CHECKPOINT_NAME_LAST = "{epoch}-last"``
If you want to checkpoint every N hours, every M train batches, and/or every K val epochs,
then you should create multiple ``ModelCheckpoint`` callbacks.
If the checkpoint's ``dirpath`` changed from what it was before while resuming the training,
only ``best_model_path`` will be reloaded and a warning will be issued.
Raises:
MisconfigurationException:
If ``save_top_k`` is smaller than ``-1``,
if ``monitor`` is ``None`` and ``save_top_k`` is none of ``None``, ``-1``, and ``0``, or
if ``mode`` is none of ``"min"`` or ``"max"``.
ValueError:
If ``trainer.save_checkpoint`` is ``None``.
Example::
>>> from lightning.pytorch import Trainer
>>> from lightning.pytorch.callbacks import ModelCheckpoint
# saves checkpoints to 'my/path/' at every epoch
>>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
>>> trainer = Trainer(callbacks=[checkpoint_callback])
# save epoch and val_loss in name
# saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt
>>> checkpoint_callback = ModelCheckpoint(
... monitor='val_loss',
... dirpath='my/path/',
... filename='sample-mnist-{epoch:02d}-{val_loss:.2f}'
... )
# save epoch and val_loss in name, but specify the formatting yourself (e.g. to avoid problems with Tensorboard
# or Neptune, due to the presence of characters like '=' or '/')
# saves a file like: my/path/sample-mnist-epoch02-val_loss0.32.ckpt
>>> checkpoint_callback = ModelCheckpoint(
... monitor='val/loss',
... dirpath='my/path/',
... filename='sample-mnist-epoch{epoch:02d}-val_loss{val/loss:.2f}',
... auto_insert_metric_name=False
... )
# retrieve the best checkpoint after training
checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
trainer = Trainer(callbacks=[checkpoint_callback])
model = ...
trainer.fit(model)
checkpoint_callback.best_model_path
.. tip:: Saving and restoring multiple checkpoint callbacks at the same time is supported under variation in the
following arguments:
*monitor, mode, every_n_train_steps, every_n_epochs, train_time_interval*
Read more: :ref:`Persisting Callback State <extensions/callbacks_state:save callback state>`
"""
CHECKPOINT_JOIN_CHAR = "-"
CHECKPOINT_EQUALS_CHAR = "="
CHECKPOINT_NAME_LAST = "last"
FILE_EXTENSION = ".ckpt"
STARTING_VERSION = 1
def __init__(
self,
dirpath: Optional[_PATH] = None,
filename: Optional[str] = None,
monitor: Optional[str] = None,
verbose: bool = False,
save_last: Optional[bool] = None,
save_top_k: int = 1,
save_weights_only: bool = False,
mode: str = "min",
auto_insert_metric_name: bool = True,
every_n_train_steps: Optional[int] = None,
train_time_interval: Optional[timedelta] = None,
every_n_epochs: Optional[int] = None,
save_on_train_epoch_end: Optional[bool] = None,
enable_version_counter: bool = True,
):
super().__init__()
self.monitor = monitor
self.verbose = verbose
self.save_last = save_last
self.save_top_k = save_top_k
self.save_weights_only = save_weights_only
self.auto_insert_metric_name = auto_insert_metric_name
self._save_on_train_epoch_end = save_on_train_epoch_end
self._enable_version_counter = enable_version_counter
self._last_global_step_saved = 0 # no need to save when no steps were taken
self._last_time_checked: Optional[float] = None
self.current_score: Optional[Tensor] = None
self.best_k_models: Dict[str, Tensor] = {}
self.kth_best_model_path = ""
self.best_model_score: Optional[Tensor] = None
self.best_model_path = ""
self.last_model_path = ""
self._last_checkpoint_saved = ""
self.kth_value: Tensor
self.dirpath: Optional[_PATH]
self.__init_monitor_mode(mode)
self.__init_ckpt_dir(dirpath, filename)
self.__init_triggers(every_n_train_steps, every_n_epochs, train_time_interval)
self.__validate_init_configuration()
@property
def state_key(self) -> str:
return self._generate_state_key(
monitor=self.monitor,
mode=self.mode,
every_n_train_steps=self._every_n_train_steps,
every_n_epochs=self._every_n_epochs,
train_time_interval=self._train_time_interval,
)
[docs] def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None:
dirpath = self.__resolve_ckpt_dir(trainer)
dirpath = trainer.strategy.broadcast(dirpath)
self.dirpath = dirpath
self._fs = get_filesystem(self.dirpath or "")
if trainer.is_global_zero and stage == "fit":
self.__warn_if_dir_not_empty(self.dirpath)
[docs] def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
self._last_time_checked = time.monotonic()
[docs] def on_train_batch_end(
self,
trainer: "pl.Trainer",
pl_module: "pl.LightningModule",
outputs: STEP_OUTPUT,
batch: Any,
batch_idx: int,
) -> None:
"""Save checkpoint on train batch end if we meet the criteria for `every_n_train_steps`"""
if self._should_skip_saving_checkpoint(trainer):
return
skip_batch = self._every_n_train_steps < 1 or (trainer.global_step % self._every_n_train_steps != 0)
train_time_interval = self._train_time_interval
skip_time = True
now = time.monotonic()
if train_time_interval:
prev_time_check = self._last_time_checked
skip_time = prev_time_check is None or (now - prev_time_check) < train_time_interval.total_seconds()
# in case we have time differences across ranks
# broadcast the decision on whether to checkpoint from rank 0 to avoid possible hangs
skip_time = trainer.strategy.broadcast(skip_time)
if skip_batch and skip_time:
return
if not skip_time:
self._last_time_checked = now
monitor_candidates = self._monitor_candidates(trainer)
self._save_topk_checkpoint(trainer, monitor_candidates)
self._save_last_checkpoint(trainer, monitor_candidates)
[docs] def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Save a checkpoint at the end of the training epoch."""
if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer):
monitor_candidates = self._monitor_candidates(trainer)
if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0:
self._save_topk_checkpoint(trainer, monitor_candidates)
self._save_last_checkpoint(trainer, monitor_candidates)
[docs] def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
"""Save a checkpoint at the end of the validation stage."""
if not self._should_skip_saving_checkpoint(trainer) and not self._should_save_on_train_epoch_end(trainer):
monitor_candidates = self._monitor_candidates(trainer)
if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0:
self._save_topk_checkpoint(trainer, monitor_candidates)
self._save_last_checkpoint(trainer, monitor_candidates)
[docs] def state_dict(self) -> Dict[str, Any]:
return {
"monitor": self.monitor,
"best_model_score": self.best_model_score,
"best_model_path": self.best_model_path,
"current_score": self.current_score,
"dirpath": self.dirpath,
"best_k_models": self.best_k_models,
"kth_best_model_path": self.kth_best_model_path,
"kth_value": self.kth_value,
"last_model_path": self.last_model_path,
}
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
dirpath_from_ckpt = state_dict.get("dirpath", self.dirpath)
if self.dirpath == dirpath_from_ckpt:
self.best_model_score = state_dict["best_model_score"]
self.kth_best_model_path = state_dict.get("kth_best_model_path", self.kth_best_model_path)
self.kth_value = state_dict.get("kth_value", self.kth_value)
self.best_k_models = state_dict.get("best_k_models", self.best_k_models)
self.last_model_path = state_dict.get("last_model_path", self.last_model_path)
else:
warnings.warn(
f"The dirpath has changed from {dirpath_from_ckpt!r} to {self.dirpath!r},"
" therefore `best_model_score`, `kth_best_model_path`, `kth_value`, `last_model_path` and"
" `best_k_models` won't be reloaded. Only `best_model_path` will be reloaded."
)
self.best_model_path = state_dict["best_model_path"]
def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
if self.save_top_k == 0:
return
# validate metric
if self.monitor is not None:
if self.monitor not in monitor_candidates:
m = (
f"`ModelCheckpoint(monitor={self.monitor!r})` could not find the monitored key in the returned"
f" metrics: {list(monitor_candidates)}."
f" HINT: Did you call `log({self.monitor!r}, value)` in the `LightningModule`?"
)
if trainer.fit_loop.epoch_loop.val_loop._has_run:
raise MisconfigurationException(m)
warning_cache.warn(m)
self._save_monitor_checkpoint(trainer, monitor_candidates)
else:
self._save_none_monitor_checkpoint(trainer, monitor_candidates)
def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
trainer.save_checkpoint(filepath, self.save_weights_only)
self._last_global_step_saved = trainer.global_step
self._last_checkpoint_saved = filepath
# notify loggers
if trainer.is_global_zero:
for logger in trainer.loggers:
logger.after_save_checkpoint(proxy(self))
@staticmethod
def _link_checkpoint(trainer: "pl.Trainer", filepath: str, linkpath: str) -> None:
if trainer.is_global_zero:
if os.path.islink(linkpath) or os.path.isfile(linkpath):
os.remove(linkpath)
elif os.path.isdir(linkpath):
shutil.rmtree(linkpath)
try:
os.symlink(filepath, linkpath)
except OSError:
# on Windows, special permissions are required to create symbolic links as a regular user
# fall back to copying the file
shutil.copy(filepath, linkpath)
trainer.strategy.barrier()
def _should_skip_saving_checkpoint(self, trainer: "pl.Trainer") -> bool:
from lightning.pytorch.trainer.states import TrainerFn
return (
bool(trainer.fast_dev_run) # disable checkpointing with fast_dev_run
or trainer.state.fn != TrainerFn.FITTING # don't save anything during non-fit
or trainer.sanity_checking # don't save anything during sanity check
or self._last_global_step_saved == trainer.global_step # already saved at the last step
)
def _should_save_on_train_epoch_end(self, trainer: "pl.Trainer") -> bool:
if self._save_on_train_epoch_end is not None:
return self._save_on_train_epoch_end
# if `check_val_every_n_epoch != 1`, we can't say when the validation dataloader will be loaded
# so let's not enforce saving at every training epoch end
if trainer.check_val_every_n_epoch != 1:
return False
# no validation means save on train epoch end
num_val_batches = (
sum(trainer.num_val_batches) if isinstance(trainer.num_val_batches, list) else trainer.num_val_batches
)
if num_val_batches == 0:
return True
# if the user runs validation multiple times per training epoch, then we run after validation
# instead of on train epoch end
return trainer.val_check_interval == 1.0
def __validate_init_configuration(self) -> None:
if self.save_top_k < -1:
raise MisconfigurationException(f"Invalid value for save_top_k={self.save_top_k}. Must be >= -1")
if self._every_n_train_steps < 0:
raise MisconfigurationException(
f"Invalid value for every_n_train_steps={self._every_n_train_steps}. Must be >= 0"
)
if self._every_n_epochs < 0:
raise MisconfigurationException(f"Invalid value for every_n_epochs={self._every_n_epochs}. Must be >= 0")
every_n_train_steps_triggered = self._every_n_train_steps >= 1
every_n_epochs_triggered = self._every_n_epochs >= 1
train_time_interval_triggered = self._train_time_interval is not None
if every_n_train_steps_triggered + every_n_epochs_triggered + train_time_interval_triggered > 1:
raise MisconfigurationException(
f"Combination of parameters every_n_train_steps={self._every_n_train_steps}, "
f"every_n_epochs={self._every_n_epochs} and train_time_interval={self._train_time_interval} "
"should be mutually exclusive."
)
if self.monitor is None and self.save_top_k not in (-1, 0, 1):
# -1: save all epochs, 0: nothing is saved, 1: save last epoch
raise MisconfigurationException(
f"ModelCheckpoint(save_top_k={self.save_top_k}, monitor=None) is not a valid"
" configuration. No quantity for top_k to track."
)
def __init_ckpt_dir(self, dirpath: Optional[_PATH], filename: Optional[str]) -> None:
self._fs = get_filesystem(dirpath if dirpath else "")
if dirpath and self._fs.protocol == "file":
dirpath = os.path.realpath(dirpath)
self.dirpath = dirpath
self.filename = filename
def __init_monitor_mode(self, mode: str) -> None:
torch_inf = torch.tensor(torch.inf)
mode_dict = {"min": (torch_inf, "min"), "max": (-torch_inf, "max")}
if mode not in mode_dict:
raise MisconfigurationException(f"`mode` can be {', '.join(mode_dict.keys())} but got {mode}")
self.kth_value, self.mode = mode_dict[mode]
def __init_triggers(
self,
every_n_train_steps: Optional[int],
every_n_epochs: Optional[int],
train_time_interval: Optional[timedelta],
) -> None:
# Default to running once after each validation epoch if neither
# every_n_train_steps nor every_n_epochs is set
if every_n_train_steps is None and every_n_epochs is None and train_time_interval is None:
every_n_epochs = 1
every_n_train_steps = 0
log.debug("Both every_n_train_steps and every_n_epochs are not set. Setting every_n_epochs=1")
else:
every_n_epochs = every_n_epochs or 0
every_n_train_steps = every_n_train_steps or 0
self._train_time_interval: Optional[timedelta] = train_time_interval
self._every_n_epochs: int = every_n_epochs
self._every_n_train_steps: int = every_n_train_steps
@property
def every_n_epochs(self) -> Optional[int]:
return self._every_n_epochs
def check_monitor_top_k(self, trainer: "pl.Trainer", current: Optional[Tensor] = None) -> bool:
if current is None:
return False
if self.save_top_k == -1:
return True
less_than_k_models = len(self.best_k_models) < self.save_top_k
if less_than_k_models:
return True
monitor_op = {"min": torch.lt, "max": torch.gt}[self.mode]
should_update_best_and_save = monitor_op(current, self.best_k_models[self.kth_best_model_path])
# If using multiple devices, make sure all processes are unanimous on the decision.
should_update_best_and_save = trainer.strategy.reduce_boolean_decision(bool(should_update_best_and_save))
return should_update_best_and_save
@classmethod
def _format_checkpoint_name(
cls,
filename: Optional[str],
metrics: Dict[str, Tensor],
prefix: str = "",
auto_insert_metric_name: bool = True,
) -> str:
if not filename:
# filename is not set, use default name
filename = "{epoch}" + cls.CHECKPOINT_JOIN_CHAR + "{step}"
# check and parse user passed keys in the string
groups = re.findall(r"(\{.*?)[:\}]", filename)
# sort keys from longest to shortest to avoid replacing substring
# eg: if keys are "epoch" and "epoch_test", the latter must be replaced first
groups = sorted(groups, key=lambda x: len(x), reverse=True)
for group in groups:
name = group[1:]
if auto_insert_metric_name:
filename = filename.replace(group, name + cls.CHECKPOINT_EQUALS_CHAR + "{" + name)
# support for dots: https://stackoverflow.com/a/7934969
filename = filename.replace(group, f"{{0[{name}]")
if name not in metrics:
metrics[name] = torch.tensor(0)
filename = filename.format(metrics)
if prefix:
filename = cls.CHECKPOINT_JOIN_CHAR.join([prefix, filename])
return filename
def __resolve_ckpt_dir(self, trainer: "pl.Trainer") -> _PATH:
"""Determines model checkpoint save directory at runtime. Reference attributes from the trainer's logger to
determine where to save checkpoints. The path for saving weights is set in this priority:
1. The ``ModelCheckpoint``'s ``dirpath`` if passed in
2. The ``Logger``'s ``log_dir`` if the trainer has loggers
3. The ``Trainer``'s ``default_root_dir`` if the trainer has no loggers
The path gets extended with subdirectory "checkpoints".
"""
if self.dirpath is not None:
# short circuit if dirpath was passed to ModelCheckpoint
return self.dirpath
if len(trainer.loggers) > 0:
if trainer.loggers[0].save_dir is not None:
save_dir = trainer.loggers[0].save_dir
else:
save_dir = trainer.default_root_dir
name = trainer.loggers[0].name
version = trainer.loggers[0].version
version = version if isinstance(version, str) else f"version_{version}"
ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
else:
# if no loggers, use default_root_dir
ckpt_path = os.path.join(trainer.default_root_dir, "checkpoints")
return ckpt_path
def _find_last_checkpoints(self, trainer: "pl.Trainer") -> Set[str]:
# find all checkpoints in the folder
ckpt_path = self.__resolve_ckpt_dir(trainer)
if self._fs.exists(ckpt_path):
return {
os.path.normpath(p)
for p in self._fs.ls(ckpt_path, detail=False)
if self.CHECKPOINT_NAME_LAST in os.path.split(p)[1]
}
return set()
def __warn_if_dir_not_empty(self, dirpath: _PATH) -> None:
if self.save_top_k != 0 and _is_dir(self._fs, dirpath, strict=True) and len(self._fs.ls(dirpath)) > 0:
rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
def _get_metric_interpolated_filepath_name(
self, monitor_candidates: Dict[str, Tensor], trainer: "pl.Trainer", del_filepath: Optional[str] = None
) -> str:
filepath = self.format_checkpoint_name(monitor_candidates)
if self._enable_version_counter:
version_cnt = self.STARTING_VERSION
while self.file_exists(filepath, trainer) and filepath != del_filepath:
filepath = self.format_checkpoint_name(monitor_candidates, ver=version_cnt)
version_cnt += 1
return filepath
def _monitor_candidates(self, trainer: "pl.Trainer") -> Dict[str, Tensor]:
monitor_candidates = deepcopy(trainer.callback_metrics)
# cast to int if necessary because `self.log("epoch", 123)` will convert it to float. if it's not a tensor
# or does not exist we overwrite it as it's likely an error
epoch = monitor_candidates.get("epoch")
monitor_candidates["epoch"] = epoch.int() if isinstance(epoch, Tensor) else torch.tensor(trainer.current_epoch)
step = monitor_candidates.get("step")
monitor_candidates["step"] = step.int() if isinstance(step, Tensor) else torch.tensor(trainer.global_step)
return monitor_candidates
def _save_last_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
if not self.save_last:
return
filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST)
if self._enable_version_counter:
version_cnt = self.STARTING_VERSION
while self.file_exists(filepath, trainer) and filepath != self.last_model_path:
filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST, ver=version_cnt)
version_cnt += 1
# set the last model path before saving because it will be part of the state.
previous, self.last_model_path = self.last_model_path, filepath
if self._fs.protocol == "file" and self._last_checkpoint_saved and self.save_top_k != 0:
self._link_checkpoint(trainer, self._last_checkpoint_saved, filepath)
else:
self._save_checkpoint(trainer, filepath)
if previous and self._should_remove_checkpoint(trainer, previous, filepath):
self._remove_checkpoint(trainer, previous)
def _save_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
assert self.monitor
current = monitor_candidates.get(self.monitor)
if self.check_monitor_top_k(trainer, current):
assert current is not None
self._update_best_and_save(current, trainer, monitor_candidates)
elif self.verbose:
epoch = monitor_candidates["epoch"]
step = monitor_candidates["step"]
rank_zero_info(f"Epoch {epoch:d}, global step {step:d}: {self.monitor!r} was not in top {self.save_top_k}")
def _save_none_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]) -> None:
filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer)
# set the best model path before saving because it will be part of the state.
previous, self.best_model_path = self.best_model_path, filepath
self._save_checkpoint(trainer, filepath)
if self.save_top_k == 1 and previous and self._should_remove_checkpoint(trainer, previous, filepath):
self._remove_checkpoint(trainer, previous)
def _update_best_and_save(
self, current: Tensor, trainer: "pl.Trainer", monitor_candidates: Dict[str, Tensor]
) -> None:
k = len(self.best_k_models) + 1 if self.save_top_k == -1 else self.save_top_k
del_filepath = None
if len(self.best_k_models) == k and k > 0:
del_filepath = self.kth_best_model_path
self.best_k_models.pop(del_filepath)
# do not save nan, replace with +/- inf
if isinstance(current, Tensor) and torch.isnan(current):
current = torch.tensor(float("inf" if self.mode == "min" else "-inf"), device=current.device)
filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, del_filepath)
# save the current score
self.current_score = current
self.best_k_models[filepath] = current
if len(self.best_k_models) == k:
# monitor dict has reached k elements
_op = max if self.mode == "min" else min
self.kth_best_model_path = _op(self.best_k_models, key=self.best_k_models.get) # type: ignore[arg-type]
self.kth_value = self.best_k_models[self.kth_best_model_path]
_op = min if self.mode == "min" else max
self.best_model_path = _op(self.best_k_models, key=self.best_k_models.get) # type: ignore[arg-type]
self.best_model_score = self.best_k_models[self.best_model_path]
if self.verbose:
epoch = monitor_candidates["epoch"]
step = monitor_candidates["step"]
rank_zero_info(
f"Epoch {epoch:d}, global step {step:d}: {self.monitor!r} reached {current:0.5f}"
f" (best {self.best_model_score:0.5f}), saving model to {filepath!r} as top {k}"
)
self._save_checkpoint(trainer, filepath)
if del_filepath and self._should_remove_checkpoint(trainer, del_filepath, filepath):
self._remove_checkpoint(trainer, del_filepath)
[docs] def to_yaml(self, filepath: Optional[_PATH] = None) -> None:
"""Saves the `best_k_models` dict containing the checkpoint paths with the corresponding scores to a YAML
file."""
best_k = {k: v.item() for k, v in self.best_k_models.items()}
if filepath is None:
assert self.dirpath
filepath = os.path.join(self.dirpath, "best_k_models.yaml")
with self._fs.open(filepath, "w") as fp:
yaml.dump(best_k, fp)
[docs] def file_exists(self, filepath: _PATH, trainer: "pl.Trainer") -> bool:
"""Checks if a file exists on rank 0 and broadcasts the result to all other ranks, preventing the internal
state to diverge between ranks."""
exists = self._fs.exists(filepath)
return trainer.strategy.broadcast(exists)
def _should_remove_checkpoint(self, trainer: "pl.Trainer", previous: str, current: str) -> bool:
"""Checks if the previous checkpoint should be deleted.
A checkpoint won't be deleted if any of the cases apply:
- The previous checkpoint is the same as the current checkpoint
- The previous checkpoint is not in the current checkpoint directory and the filesystem is local
- The previous checkpoint is the checkpoint the Trainer resumed from and the filesystem is local
"""
if previous == current:
return False
if self._fs.protocol != "file":
return True
previous = Path(previous).absolute()
resume_path = Path(trainer.ckpt_path).absolute() if trainer.ckpt_path is not None else None
if resume_path is not None and previous == resume_path:
return False
assert self.dirpath is not None
dirpath = Path(self.dirpath).absolute()
return dirpath in previous.parents
def _remove_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
"""Calls the strategy to remove the checkpoint file."""
trainer.strategy.remove_checkpoint(filepath)
